Risk factors and survival prediction model establishment for prognosis in patients with radical resection of gallbladder cancer

BACKGROUND Gallbladder cancer(GBC)is the most common malignant tumor of the biliary system,and is often undetected until advanced stages,making curative surgery unfeasible for many patients.Curative surgery remains the only option for long-term survival.Accurate postsurgical prognosis is crucial for effective treatment planning.tumor-node-metastasis staging,which focuses on tumor infiltration,lymph node metastasis,and distant metastasis,limits the accuracy of prognosis.Nomograms offer a more comprehensive and personalized approach by visually analyzing a broader range of prognostic factors,enhancing the precision of treatment planning for patients with GBC.AIM A retrospective study analyzed the clinical and pathological data of 93 patients who underwent radical surgery for GBC at Peking University People's Hospital from January 2015 to December 2020.Kaplan-Meier analysis was used to calculate the 1-,2-and 3-year survival rates.The log-rank test was used to evaluate factors impacting prognosis,with survival curves plotted for significant variables.Single-factor analysis revealed statistically significant differences,and multivariate Cox regression identified independent prognostic factors.A nomogram was developed and validated with receiver operating characteristic curves and calibration curves.Among 93 patients who underwent radical surgery for GBC,30 patients survived,accounting for 32.26%of the sample,with a median survival time of 38 months.The 1-year,2-year,and 3-year survival rates were 83.87%,68.82%,and 53.57%,respectively.Univariate analysis revealed that carbohydrate antigen 19-9 expre-ssion,T stage,lymph node metastasis,histological differentiation,surgical margins,and invasion of the liver,ex-trahepatic bile duct,nerves,and vessels(P≤0.001)significantly impacted patient prognosis after curative surgery.Multivariate Cox regression identified lymph node metastasis(P=0.03),histological differentiation(P<0.05),nerve invasion(P=0.036),and extrahepatic bile duct invasion(P=0.014)as independent risk factors.A nomogram model with a concordance index of 0.838 was developed.Internal validation confirmed the model's consistency in predicting the 1-year,2-year,and 3-year survival rates.CONCLUSION Lymph node metastasis,tumor differentiation,extrahepatic bile duct invasion,and perineural invasion are independent risk factors.A nomogram based on these factors can be used to personalize and improve treatment strategies.

Parametric Cure Model versus Proportional Hazards Model in Survival Analysis of Breast Cancer and Other Malignancies

As cancer therapy has progressed dramatically, its goal has shifted toward cure of the disease (curative therapy) rather than prolongation of time to death (life-prolonging therapy). Consequently, the proportion of cured patients (c) has become an important measure of the long-term survival benefit derived from therapy. In 1949, Boag addressed this issue by developing the parametric log-normal cure model, which provides estimates of c and m where m is the mean of log times to death from cancer among uncured patients. Unfortunately, traditional methods based on the proportional hazards model like the Cox regression and log-rank tests cannot provide an estimate of either c or m. Rather, these methods estimate only the differences in hazard between two or more groups. In order to evaluate the long-term validity and usefulness of the parametric cure model compared with the proportional hazards model, we reappraised randomized controlled trials and simulation studies of breast cancer and other malignancies. The results reveal that: 1) the traditional methods fail to distinguish between curative and life-prolonging therapies;2) in certain clinical settings, these methods may favor life-prolonging treatment over curative treatment, giving clinicians a false estimate of the best regimen;3) although the Boag model is less sensitive to differences in failure time when follow-up is limited, it gains power as more failures occur. In conclusion, unless the disease is always fatal, the primary measure of survival benefit should be c rather than m or hazard ratio. Thus, the Boag lognormal cure model provides more accurate and more useful insight into the long-term benefit of cancer treatment than the traditional alternatives.

Bayesian Joint Modelling of Survival Time and Longitudinal CD4 Cell Counts Using Accelerated Failure Time and Generalized Error Distributions

Survival of HIV/AIDS patients is crucially dependent on comprehensive and targeted medical interventions such as supply of antiretroviral therapy and monitoring disease progression with CD4 T-cell counts. Statistical modelling approaches are helpful towards this goal. This study aims at developing Bayesian joint models with assumed generalized error distribution (GED) for the longitudinal CD4 data and two accelerated failure time distributions, Lognormal and loglogistic, for the survival time of HIV/AIDS patients. Data are obtained from patients under antiretroviral therapy follow-up at Shashemene referral hospital during January 2006-January 2012 and at Bale Robe general hospital during January 2008-March 2015. The Bayesian joint models are defined through latent variables and association parameters and with specified non-informative prior distributions for the model parameters. Simulations are conducted using Gibbs sampler algorithm implemented in the WinBUGS software. The results of the analyses of the two different data sets show that distributions of measurement errors of the longitudinal CD4 variable follow the generalized error distribution with fatter tails than the normal distribution. The Bayesian joint GED loglogistic models fit better to the data sets compared to the lognormal cases. Findings reveal that patients’ health can be improved over time. Compared to the males, female patients gain more CD4 counts. Survival time of a patient is negatively affected by TB infection. Moreover, increase in number of opportunistic infection implies decline of CD4 counts. Patients’ age negatively affects the disease marker with no effects on survival time. Improving weight may improve survival time of patients. Bayesian joint models with GED and AFT distributions are found to be useful in modelling the longitudinal and survival processes. Thus we recommend the generalized error distributions for measurement errors of the longitudinal data under the Bayesian joint modelling. Further studies may investigate the models with various types of shared random effects and more covariates with predictions.

L0 Regularization for the Estimation of Piecewise Constant Hazard Rates in Survival Analysis

In a survival analysis context, we suggest a new method to estimate the piecewise constant hazard rate model. The method provides an automatic procedure to find the number and location of cut points and to estimate the hazard on each cut interval. Estimation is performed through a penalized likelihood using an adaptive ridge procedure. A bootstrap procedure is proposed in order to derive valid statistical inference taking both into account the variability of the estimate and the variability in the choice of the cut points. The new method is applied both to simulated data and to the Mayo Clinic trial on primary biliary cirrhosis. The algorithm implementation is seen to work well and to be of practical relevance.

Usage of Cox-Regression Model for Forecasting of Survival Rate in Patients with the Early Stage of Non-Small Cell Lung Cancer

In the past decades a lot of investigations were focused on searching for more accurate markers of lung cancer progression. Researchers indicate that molecular markers may be useful in forecasting of treatment outcome and overall survival rate in patients with non-small cell lung cancer. The aim of our research was to create a forecasting model in order to identify patients with stage I-II of non-small cell lung cancer and dismal prognosis. Our research covered 254 patients with the early stage of non-small cell lung cancer who underwent a cure from June 2008 till December2012 inthe Department of Thoracic Surgery of Zaporizhzhia Regional Clinical Oncologic Dispensary. Surgery was performed for all patients. Adjuvant chemotherapy was performed for 101 patients. In order to carry out multivariate Cox-regression analysis, STATISTICA 6.0 (StatSoft Inc.) program was used. The most significant from 39 variables were selected (tumor size, histological form of tumor, volume of surgical intervention, volume of conducted lymph node dissection, Ki-67 expression, EGFR expression, E-cadherin expression). We propose the computer system which can forecast survival rate in patients with the early stage of non-small cell lung cancer.

Evaluation of factors affecting the success rate of orthodontic mini-implants by survival analysis

AIM: To investigate the success rate of mini-implants and its characteristics and risk factors by survival analyses. METHODS: Three hundred and ninety-four miniimplants of the same type were placed by a single clinician. Age, gender, treatment duration, time of failure, side and jaw of implantation and the soft tissue at placement site were recorded. Odds ratio, survival curves, and Cox proportional hazard model were applied to evaluate the factors influencing the miniimplants' success rate. RESULTS: The cumulative success rate was 88.1%.The maxilla had a significantly higher success rate than that of the mandible(91.7% vs 83.7%, respectively, P = 0.019). Placement of mini-implants in the attached gingiva(AG) showed a higher success rate than that of the mucogingival junction(MGJ) and mucous membrane(MM)(AG, 94.3%; MGJ, 85.8%; MM, 79.4%; P < 0.001). Significant association was found between the jaw and the gingival tissue type(P < 0.001). There were no significant differences between maxilla and mandible when compared within each placement site.CONCLUSION: The gingival tissue type had the most significant effect on the success rate of the mini-implant with higher success rate in the attached gingiva.

A New Statistical Modeling Approach for Survival Analysis of Cancer Patients—Multiple Myeloma Cancer

Background: The Cox Proportional Hazard (Cox-PH) model has been a popularly used method for survival analysis of cancer data given the survival times as a function of covariates or risk factors. However, it is very seldom to see the assumptions for the application of the Cox-PH model satisfied in most of the research studies, raising questions about the effectiveness, robustness, and accuracy of the model predicting the proportion of survival times. This is because the necessary assumptions in most cases are difficult to satisfy, as well as the assessment of interaction among covariates. Methods: To further improve the therapeutic/treatment strategy for cancer diseases, we proposed a new approach to survival analysis using multiple myeloma (MM) cancer data. We first developed a data-driven nonlinear statistical model that predicts the survival times with 93% accuracy. We then performed a parametric analysis on the predicted survival times to obtain the survival function which is used in estimating the proportion of survival times. Results: The new proposed approach for survival analysis has proved to be more robust and gives better estimates of the proportion of survival than the Cox-PH model. Also, satisfying the proposed model assumptions and finding interactions among risk factors is less difficult compared to the Cox-PH model. The proposed model can predict the real values of the survival times and the identified risk factors are ranked according to the percent of contribution to the survival time. Conclusion: The new proposed nonlinear statistical model approach for survival analysis of cancer diseases is very efficient and provides an improved and innovative strategy for cancer therapeutic/treatment.

Cancer incidence and patient survival rates among the residents in the Pudong New Area of Shanghai between 2002 and 2006

With the growing threat of malignancy to health,it is necessary to analyze cancer incidence and patient survival rates among the residents in Pudong New Area of Shanghai to formulate better cancer prevention strategies.A total of 43,613 cancer patients diagnosed between 2002 and 2006 were recruited from the Pudong New Area Cancer Registry.The incidence,observed survival rate,and relative survival rate of patients grouped by sex,age,geographic area,and TNM stage were calculated using the Kaplan-Meier,life table,and Ederer II methods,respectively.Between 2002 and 2006,cancer incidence in Pudong New Area was 349.99 per 100,000 person-years,and the 10 most frequently diseased sites were the lung,stomach,colon and rectum,liver,breast,esophagus,pancreas,brain and central nervous system,thyroid,and bladder.For patients with cancers of the colon and rectum,breast,thyroid,brain and central nervous system,and bladder,the 5-year relative survival rate was greater than 40%,whereas patients with cancers of the liver and pancreas had a 5-year relative survival rate of less than 10%.The 1-year to 5-year survival rates for patients grouped by sex,age,geographic area,and TNM stage differed significantly(all P<0.001).Our results indicate that cancer incidence and patient survival in Pudong New Area vary by tumor type,sex,age,geographic area,and TNM stage.

Comparison of Cox proportional hazards model,Cox proportional hazards with time-varying coefficients model,and lognormal accelerated failure time model:Application in time to event analysis of melioidosis patients

Objective:To compare the prognostic factors of mortality among melioidosis patients between lognormal accelerated failure time(AFT),Cox proportional hazards(PH),and Cox PH with time-varying coefficient(TVC)models.Methods:A retrospective study was conducted from 2014 to 2019 among 453 patients who were admitted to Hospital Sultanah Bahiyah,Kedah and Hospital Tuanku Fauziah,Perlis in Northern Malaysia due to confirmed-cultured melioidosis.The prognostic factors of mortality from melioidosis were obtained from AFT survival analysis,and Cox’s models and the findings were compared by using the goodness of fit methods.The analyses were done by using Stata SE version 14.0.Results:A total of 242 patients(53.4%)survived.In this study,the median survival time of melioidosis patients was 30.0 days(95%CI 0.0-60.9).Six significant prognostic factors were identified in the Cox PH model and Cox PH-TVC model.In AFT survival analysis,a total of seven significant prognostic factors were identified.The results were found to be only a slight difference between the identified prognostic factors among the models.AFT survival showed better results compared to Cox's models,with the lowest Akaike information criteria and best fitted Cox-snell residuals.Conclusions:AFT survival analysis provides more reliable results and can be used as an alternative statistical analysis for determining the prognostic factors of mortality in melioidosis patients in certain situations.

Construction of a clinical survival prognostic model for middle-aged and elderly patients with stage III rectal adenocarcinoma

BACKGROUND Nomograms for prognosis prediction in colorectal cancer patients are few,and prognostic indicators differ with age.AIM To construct a new nomogram survival prediction tool for middle-aged and elderly patients with stage III rectal adenocarcinoma.METHODS A total of 2773 eligible patients were divided into the training cohort(70%)and the validation cohort(30%).Optimal cutoff values were calculated using the X-tile software for continuous variables.Univariate and multivariate Cox proportional hazards regression analyses were used to determine overall survival(OS)and cancer-specific survival(CSS)-related prognostic factors.Two nomograms were successfully constructed.The discriminant and predictive ability and clinical usefulness of the model were also assessed by multiple methods of analysis.RESULTS The 95%CI in the training group was 0.719(0.690-0.749)and 0.733(0.702-0.74),while that in the validation group was 0.739(0.696-0.782)and 0.750(0.701-0.800)for the OS and CSS nomogram prediction models,respectively.In the validation group,the AUC of the three-year survival rate was 0.762 and 0.770,while the AUC of the five-year survival rate was 0.722 and 0.744 for the OS and CSS nomograms,respectively.The nomogram distinguishes all-cause mortality from cancer-specific mortality in patients with different risk grades.The time-dependent AUC and decision curve analysis showed that the nomogram had good clinical predictive ability and decision efficacy and was significantly better than the tumor-node-metastases staging system.CONCLUSION The survival prediction model constructed in this study is helpful in evaluating the prognosis of patients and can aid physicians in clinical diagnosis and treatment.

Artificial Neural Network Model for Predicting Lung Cancer Survival

The object of our present study is to develop a piecewise constant hazard model by using an Artificial Neural Network (ANN) to capture the complex shapes of the hazard functions, which cannot be achieved with conventional survival analysis models like Cox proportional hazard. We propose a more convenient approach to the PEANN created by Fornili et al. to handle a large amount of data. In particular, it provides much better prediction accuracies over both the Poisson regression and generalized estimating equations. This has been demonstrated with lung cancer patient data taken from the Surveillance, Epidemiology and End Results (SEER) program. The quality of the proposed model is evaluated by using several error measurement criteria.

Analysis of Influencing Factors on Survival Time of Patients with Heart Failure

To explore the influencing factors of survival time of patients with heart failure, a total of 1789 patients with heart failure were collected from Shanghai Shuguang Hospital. The Cox proportional hazards model and the mixed effects Cox model were used to analyze the factors on survival time of patients. The results of Cox proportional hazards model showed that age (RR = 1.32), hypertension (RR = 0.67), ARB (RR = 0.55), diuretic (RR = 1.48) and antiplatelet (RR = 0.53) have significant impacts on the survival time of patients. The results of mixed effects Cox model showed that age (RR = 1.16), hypertension (RR = 0.61), lung infection (RR = 1.43), ARB (RR = 0.64), β-blockers (RR = 0.77) and antiplatelet (RR = 0.69) have a significant impact on the survival time of patients. The results are consistent with the covariates age, hypertension, ARB and antiplatelet but inconsistent with the covariates lung infection and β-blockers.

Forecasting of Survival Rate in Patients with the Early Stage of Non Small Cell Lung Cancer

Lung cancer is the most common cause of death from oncological diseases all over the world. Primary treatment of patients with the early stage of non-small cell lung cancer is a surgery. However, after surgery 30% - 85% of patients undergo disease progression. In order to improve the results of treatment of patients with non-small cell lung cancer it is necessary to separate a group of patients with dismal prognosis for whom adjuvant chemotherapy will permit improving the survival rate. The aim of our research was to create a forecasting model with a view to detect the patients with the early stage of non-small cell lung cancer and dismal prognosis. Our research covered 254 patients with the early stage of non-small cell lung cancer who underwent a cure from June 2008 till December 2012 in the department of thoracic surgery of Zaporizhzhia Regional Clinical Oncologic Dispensary. In order to identify the factors connected with the risks of low survival rate of patients with the early stage of non-small cell lung cancer after curative treatment (surgical treatment, adjuvant chemotherapy), a method of design of neural network models of classification was used. 39 factors were taken for input characteristics. During investigation two forecasting models were built. As follows from the analysis of first forecasting model with the increase of the patient’s BMI, the risk of low patient survival rate statistically and significantly (p = 0.03) decreases, OR = 0.89 (95% CI 0.80 - 0.99) for each kg/m2 index value. The risk of low patient survival rate also decreases (p = 0.02) if he has a squamous cell carcinoma, OR = 0.36 (95% CI 0.15 - 0.88) compared with other histological forms of tumor. The connection between the risk of low patient survival rate and the volume of surgical intervention was discovered (p = 0.01), OR = 3.19 (95% CI 1.29 - 7.86) for patients who underwent a pulmonectomy compared with patients who underwent an upper bilobectomy. As follows from the analysis of second forecasting model with the increase of the patient’s BMI the risk of low patient survival rate statistically and significantly (p = 0.01) decreases;OR = 0.84 (95% CI 0.74 - 0.96) for each kg/m2 index value. It is found that with the increasing level of EGFR expression in the primary tumor, the risk of low patient survival rate statistically and significantly increases (p = 0.04), OR = 1.39 (95% CI 1.01 - 1.90) for each graduation rate. The risk of low patient survival rate also increases when conducting the lymph dissection in the volume D0 - D1.

A Measure for Assessing Functions of Time-Varying Effects in Survival Analysis

A standard approach for analyses of survival data is the Cox proportional hazards model. It assumes that covariate effects are constant over time, i.e. that the hazards are proportional. With longer follow-up times, though, the effect of a variable often gets weaker and the proportional hazards (PH) assumption is violated. In the last years, several approaches have been proposed to detect and model such time-varying effects. However, comparison and evaluation of the various approaches is difficult. A suitable measure is needed that quantifies the difference between time-varying effects and enables judgement about which method is best, i.e. which estimate is closest to the true effect. In this paper we adapt a measure proposed for the area between smoothed curves of exposure to time-varying effects. This measure is based on the weighted area between curves of time-varying effects relative to the area under a reference function that represents the true effect. We introduce several weighting schemes and demonstrate the application and performance of this new measure in a real-life data set and a simulation study.

Survival Analysis of Lung Cancer Patients from TCGA Cohort

Lung cancer is one of the leading causes of death worldwide, accounting for an estimated 2.1 million cases in 2018. To analyze the risk factors behind the lung cancer survival, this paper employs two main models: Kaplan-Meier estimator and Cox proportional hazard model [1]. Also, log-rank test and wald test are utilized to test whether a correlation exists or not, which is discussed in detail in later parts of the paper. The aim is to find out the most influential factors for the survival probability of lung cancer patients. To summarize the results, stage of cancer is always a significant factor for lung cancer survival, and time has to be taken into account when analyzing the survival rate of patients in our data sample, which is from TCGA. Future study on lung cancer is also required to make improvement for the treatment of lung cancer, as our data sample might not represent the overall condition of patients diagnosed with lung cancer;also, more appropriate and advanced models should be employed in order to reflect factors that can affect survival rate of patients with lung cancer in detail.

Survival Analysis of Factor Affects Survival Time of Hypertension Patients

Hypertension is a major long-term health condition and a leading modifiable risk factor for cardiovascular disease and death. The aim of this study was to examine major factors that affect survival time of hypertension patients under follow-up. We considered a total of 430 random samples of hypertension patients who had been under follow up at Yekatit-12 Hospital in Ethiopia from January 2013 to January 2019. Four parametric accelerated failure time distributions: Exponential, Weibull, Lognormal and loglogistic are used to analyse survival probabilities of the patients. The Kaplan-Meierestimation method and log-rank tests were used to compare the survival experience of patients with respect to different covariates. The Weibull model is selected to best fit to the data sets. The results indicate that the baseline age of the patient, place of residence, family history of hypertension, khat intake, blood cholesterol level of the patient, hypertension disease stage, adherence to the treatment and related disease were significantly associated with survival time of hypertension patients. But factor like gender, tobacco use, alcohol use, diabetes mellitus status and fasting blood sugar were not significantly associated factors. Society and all stakeholders should be aware of the consequences of these factors which can influence the survival time of hypertension patients.

Applications of Dynamic-Equilibrium Continuous Markov Stochastic Processes to Elements of Survival Analysis

In this article, we summarize some results on invariant non-homogeneous and dynamic-equilibrium (DE) continuous Markov stochastic processes. Moreover, we discuss a few examples and consider a new application of DE processes to elements of survival analysis. These elements concern the stochastic quadratic-hazard-rate model, for which our work 1) generalizes the reading of its It? stochastic ordinary differential equation (ISODE) for the hazard-rate-driving independent (HRDI) variables, 2) specifies key properties of the hazard-rate function, and in particular, reveals that the baseline value of the HRDI variables is the expectation of the DE solution of the ISODE, 3) suggests practical settings for obtaining multi-dimensional probability densities necessary for consistent and systematic reconstruction of missing data by Gibbs sampling and 4) further develops the corresponding line of modeling. The resulting advantages are emphasized in connection with the framework of clinical trials of chronic obstructive pulmonary disease (COPD) where we propose the use of an endpoint reflecting the narrowing of airways. This endpoint is based on a fairly compact geometric model that quantifies the course of the obstruction, shows how it is associated with the hazard rate, and clarifies why it is life-threatening. The work also suggests a few directions for future research.

Incidence and survival of stomach cancer in a high-risk population of Chile

AIM:To study the incidence and survival rate of stomach cancer(SC)and its associated factors in a high risk population in Chile. METHODS:The population-based cancer registry of Valdivia,included in the International Agency for Research on Cancer system,covers 356 396 residents of Valdivia Province,Southern Chile.We studied all SC cases entered in this Registry during 1998-2002 (529 cases).Population data came from the Chilean census(2002).Standardized incidence rates per 100 000 inhabitants(SIR)using the world population, cumulative risk of developing cancer before age 75, and rate ratios by sex,age,ethnicity and social factors were estimated.Relative survival(EdererⅡmethod) and age-standardized estimates(Brenner method) were calculated.Specific survival rates(Kaplan-Meier) were measured at 3 and 5 years and survival curves were analyzed with the Logrank and Breslow tests. Survival was studied in relation to demographics, clinical presentation,laboratory results and medical management of the cases.Those variables significantly associated with survival were later included in a Cox multivariate model. RESULTS:Between 1998 and 2002,529 primary gastric cancers occurred in Valdivia(crude incidence rate 29.2 per 100000 inhabitants).Most cases were male(69.0%), residents of urban areas(57.5%)and Hispanic(83.2%), with a low education level(84.5%<8 school years). SC SIR was higher in men than women(40.8 and 14.8 respectively,P<0.001),risk factors were low education RR 4.4(95%CI:2.9-6.8)and 1.6,(95%CI:1.1-2.1) for women and men respectively and Mapuche ethnicity only significant for women(RR 2.2,95%CI:1.2-3.7).Of all cases,76.4%were histologically confirmed,11.5% had a death certificate only(DCO),56.1%were TNM stageⅣ;445 cases(84.1%)were eligible for survival analysis,all completed five years follow-up;42 remained alive,392 died of SC and 11 died from other causes. Specific 5-year survival,excluding cases with DCO,was 10.6%(95%CI:7.7-13.5);5-year relative survival rate was 12.3%(95%CI:9.1-16.1),men 10.9%(95%CI: 7.4-15.2)and women 16.1%(95%CI:9.5-24.5).Fiveyear specific survival was higher for patients aged<55 years(17.3%),with intestinal type of cancer(14.6%), without metastasis(22.2%),tumor size<4 cm(60.0%), without lymphatic invasion(77.1%),only involvement of the mucous membrane(100%).Statistically significant independent prognostic factors were:TNM staging, diffuse type,metastasis,supraclavicular adenopathy, palpable tumor,and hepatitis or ascites. CONCLUSION:Social determinants are the main risk factors for SC,but not for survival.An advanced clinical stage at consultation is the main cause of poor SC survival.

Probabilistic Model Checking-Based Survivability Analysis in Vehicle-to-Vehicle Networks

Probabilistic model checking has been widely applied to quantitative analysis of stochastic systems, e.g., analyzing the performance, reliability and survivability of computer and communication systems. In this paper, we extend the application of probabilistic model checking to the vehicle to vehicle(V2V) networks. We first develop a continuous-time Markov chain(CTMC) model for the considered V2V network, after that, the PRISM language is adopted to describe the CTMC model, and continuous-time stochastic logic is used to describe the objective survivability properties. In the analysis, two typical failures are considered, namely the node failure and the link failure, respectively induced by external malicious attacks on a target V2V node, and interrupt in a communication link. Considering these failures, their impacts on the network survivability are demonstrated. It is shown that with increasing failure strength, the network survivability is reduced. On the other hand, the network survivability can be improved with increasing repair rate. The proposed probabilistic model checking-based approach can be effectively used in survivability analysis for the V2V networks, moreover, it is anticipated that the approach can be conveniently extended to other networks.

Fibrinogen-to-albumin ratio predicts overall survival of hepatocellular carcinoma

BACKGROUND Fibrinogen-to-albumin ratio(FAR)has been found to be of prognostic significance for several types of malignant tumors.However,less is known about the association between FAR and survival outcomes in hepatocellular carcinoma(HCC)patients.AIM To explore the association between FAR and prognosis and survival in patients with HCC.METHODS A total of 366 histologically confirmed HCC patients diagnosed between 2013 and 2018 in a provincial cancer hospital in southwestern China were retrospectively selected.Relevant data were extracted from the hospital information system.The optimal cutoff for baseline serum FAR measured upon disease diagnosis was established using the receiver operating characteristic(ROC)curve.Univariate and multivariate Cox proportional hazards models were used to determine the crude and adjusted associations between FAR and the overall survival(OS)of the HCC patients while controlling for various covariates.The restricted cubic spline(RCS)was applied to estimate the dose-response trend in the FAR-OS association.RESULTS The optimal cutoff value for baseline FAR determined by the ROC was 0.081.Multivariate Cox proportional hazards model revealed that a lower baseline serum FAR level was associated with an adjusted hazard ratio of 2.43(95%confidence interval:1.87–3.15)in the OS of HCC patients,with identifiable dose-response trend in the RCS.Subgroup analysis showed that this FAR-OS association was more prominent in HCC patients with a lower baseline serum aspartate aminotransferase or carbohydrate antigen 125 level.CONCLUSION Serum FAR is a prominent prognostic indicator for HCC.Intervention measures aimed at reducing FAR might result in survival benefit for HCC patients.